Figure REF  illustrates the prediction uncertainty of neural networks in Table REF . Assume we project the examples in a 2D space. Sentence 1 lies in the region with many negative examples. Sentence 2 lies in the boundary region. Sentence 3 is far away from the ID region.
Figure REF  (a) represents the prediction by traditional neural networks with softmax and demonstrates over-confidence. It only assigns high uncertainty (entropy) near the classification boundary.
[1]} prove that ReLU type neural networks produce arbitrary high confidence predictions far away from the training data.
Figure REF  (b) represents the predictive entropy of a well-calibrated model.
Figure REF  (c) and (d) shows the evidential uncertainty decomposes the uncertainty in (b) based on different root causes. We observe high vacuity in OOD regions and high dissonance in ID boundary regions. Vacuity can effectively detect OOD samples from boundary ID examples because the cause of uncertainty is due to a lack of evidence. We can distinguish sentence 3 from sentence 2 in Figure REF  (c) but not in Figure REF  (b).
